Talks & Seminars
Title: Deep Learning on Graphs
Dr. Alex Smola, Amazon Web Services
Date & Time: May 14, 2019 14:00
Venue: Department of Computer Science and Engineering, Room No. 109, 01st Floor, New CSE/CC Building
Deep Learning has shown great success on a wide range of domains, such as text, images, audio and tabular data. For structured data on graphs deep learning remains a work in progress. In this talk I’ll cover the basics of models with vertex updates and stationary distributions. Moreover, I’ll address how to distribute and partition the problem efficiently for scalable inference. Lastly, I’ll describe how DGL.ai offers a high-level Python API to accomplish this goal, thereby allowing the algorithm designer to focus on the model while a systems designer can focus on computational efficiency.
Speaker Profile:
Dr. Alex Smola studied physics in Munich at the University of Technology, Munich, at the Universita degli Studi di Pavia and at AT&T Research in Holmdel. During this time he was at the Maximilianeum München and the Collegio Ghislieri in Pavia. In 1996 he received the Master degree at the University of Technology, Munich and in 1998 the Doctoral Degree in computer science at the University of Technology Berlin. Until 1999 he was a researcher at the IDA Group of the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer Geselschaft). After that, he worked as a Researcher and Group Leader at the Research School for Information Sciences and Engineering of the Australian National University. From 2004 onwards he worked as a Senior Principal Researcher and Program Leader at the Statistical Machine Learning Program at NICTA. From 2008 to 2012 he worked at Yahoo Research. In spring of 2012 he moved to Google Research to spend a wonderful year in Mountain View and continued working there until the end of 2014. From 2013-2017 he was professor at Carnegie Mellon University. He co-founded Marianas Labs in early 2015. In July 2016 he moved to Amazon Web Services to help build AI and Machine Learning tools for everyone.
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